Road Safety • Real-time Risk • City Intelligence
RiderSafe.AI — Two-Wheeler Safety & City Risk Intelligence
A road safety intelligence platform for rider risk analytics,
accident risk prediction, hotspot heatmaps, and
real-time safety alerts—built for cities, fleets, and mobility ecosystems.
A digital safety intelligence system that connects riders, roads, and response ecosystems into one actionable network — enabling cities and riders to predict, prevent, and respond to high-risk events using real-time signals — not only post-incident reports.
Built for multi-stakeholder environments where safety decisions must be based on consistent evidence trails — without acting as a law-enforcement, punitive, or regulatory authority.
Part of our Applied AI innovation programs at CommGenAI.
Real-time Risk Events (seconds)
Safety Score + Coaching Journeys
Hotspot Heatmaps + City Safety Index
Cities & Road Safety Missions
Fleets & Delivery Partners
Insurers & Safety Programs
OEM/Connected Mobility Partners
The Problem: Road Risks Exist — But Prevention Doesn’t
Two-wheeler ecosystems often rely on delayed incident records and fragmented awareness. Riders get feedback too late, cities lack continuous risk visibility, and safety programs struggle to measure what actually changed on the road.
- Reactive reporting — interventions start after incidents and trends are already established.
- Low signal-to-action — risk signals don’t translate into timely alerts, coaching, or routing changes.
- Fragmented data sources — rider behaviour, road hazards, and response workflows remain disconnected.
- No measurable program impact — safety campaigns struggle to prove behavioural improvements or corridor reductions.
The Core Idea: Continuous Risk Intelligence, Not “Awareness”
RiderSafe.AI creates a real-time Risk Ledger by transforming live signals into risk events → interventions → outcomes, enabling explainable safety scoring, hotspot intelligence, and measurable prevention loops for riders and city stakeholders.
Pillar 01
Realtime Risk Events
Detect high-risk patterns early (speed bursts, harsh braking, distraction cues, hazardous corridors) in near real-time.
Pillar 02
Safety Score Engine
Converts events into explainable rider/fleet safety scores and trendlines that quantify improvement over time.
Pillar 03
Prevention Loops
Nudges and coaching journeys help reduce repeat behaviours, improving safety through continuous feedback.
Pillar 04
City Insight Layer
Hotspot heatmaps, corridor risk ranking, and safety indices to guide targeted interventions and policy actions.
How It Works: Sense → Predict → Act
Step 1 — Safety Telemetry
Capture signals from rider app/device (motion, location, speed context) to build a live safety fabric.
Step 2 — AI Risk Inference
Detect risky patterns, predict incident likelihood, and score severity to trigger the right action at the right time.
Step 3 — Alerts + Insights
Deliver proactive alerts/coaching and generate hotspot intelligence, dashboards, and safety indices for city stakeholders.
What This Delivers: Safety Proof + Actionable Control
- Proactive rider alerts and micro-coaching that reduces repeat risky behaviour
- Hotspot heatmaps that identify dangerous corridors and time-bands before incidents spike
- Explainable safety scoring for riders/fleets enabling incentive programs and safer habits
- Decision-ready dashboards for city agencies to track interventions and measurable outcomes
KPI
Alert Latency
Seconds-level action
KPI
Model Accuracy
Predictive strength
KPI
Risk Reduction
Behaviour change
KPI
Hotspot Precision
Corridor clarity
KPI
Adoption
Active users
KPI
Governance
Evidence trails
Important Clarification
CommGenAI does not function as a regulator, enforcement body, or punitive system. RiderSafe.AI is an
evidence creation and prevention intelligence
platform that helps stakeholders take proactive safety actions using explainable signals and measurable outcomes.
Interested in Exploring a RiderSafe.AI Pilot?
We collaborate with cities, partners, fleets, and ecosystem stakeholders through limited-scope pilots and controlled rollouts —
no long-term commitments required.
Request a Discussion
Typical pilot: telemetry + AI risk inference + alerts + dashboards + hotspot insights